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https://github.com/langchain-ai/langgraph.git
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chore(docs): Consolidate hooks for copy markdown and notebooks (#5459)
Consolidating the hooks to avoid duplication of logic We need this change for consolidating js and python content: we need include-markdown to run as a mkdocs plugin before our pipeline (rather than as markdown extension which runs after our hooks plugin).
This commit is contained in:
Eugene Yurtsev
@@ -7,7 +7,7 @@ LangGraph provides built-in support for [LLMs (language models)](https://python.
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Use [`init_chat_model`](https://python.langchain.com/docs/how_to/chat_models_universal_init/) to initialize models:
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{!snippets/chat_model_tabs.md!}
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{% include-markdown "../../snippets/chat_model_tabs.md" %}
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### Instantiate a model directly
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@@ -1507,7 +1507,7 @@ Because many LangChain objects implement the [Runnable Protocol](https://python.
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See example below. To demonstrate async invocations of underlying LLMs, we will include a chat model:
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{!snippets/chat_model_tabs.md!}
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{% include-markdown "../../snippets/chat_model_tabs.md" %}
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```python
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from langchain.chat_models import init_chat_model
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@@ -125,7 +125,7 @@
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"memories = store.search((\"user_123\", \"memories\"), query=\"I like food?\", limit=5)\n",
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"\n",
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"for memory in memories:\n",
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" print(f'Memory: {memory.value[\"text\"]} (similarity: {memory.score})')"
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" print(f\"Memory: {memory.value['text']} (similarity: {memory.score})\")"
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]
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},
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{
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+1
-1
@@ -28,4 +28,4 @@ title: LangGraph
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}
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</style>
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{!../README.md!}
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{% include-markdown "../../README.md" %}
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@@ -0,0 +1,87 @@
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=== "OpenAI"
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```shell
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pip install -U "langchain[openai]"
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```
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```python
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import os
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from langchain.chat_models import init_chat_model
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os.environ["OPENAI_API_KEY"] = "sk-..."
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llm = init_chat_model("openai:gpt-4.1")
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```
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👉 Read the [OpenAI integration docs](https://python.langchain.com/docs/integrations/chat/openai/)
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=== "Anthropic"
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```shell
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pip install -U "langchain[anthropic]"
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```
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```python
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import os
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from langchain.chat_models import init_chat_model
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os.environ["ANTHROPIC_API_KEY"] = "sk-..."
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llm = init_chat_model("anthropic:claude-3-5-sonnet-latest")
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```
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👉 Read the [Anthropic integration docs](https://python.langchain.com/docs/integrations/chat/anthropic/)
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=== "Azure"
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```shell
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pip install -U "langchain[openai]"
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```
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```python
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import os
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from langchain.chat_models import init_chat_model
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os.environ["AZURE_OPENAI_API_KEY"] = "..."
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os.environ["AZURE_OPENAI_ENDPOINT"] = "..."
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os.environ["OPENAI_API_VERSION"] = "2025-03-01-preview"
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llm = init_chat_model(
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"azure_openai:gpt-4.1",
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azure_deployment=os.environ["AZURE_OPENAI_DEPLOYMENT_NAME"],
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)
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```
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👉 Read the [Azure integration docs](https://python.langchain.com/docs/integrations/chat/azure_chat_openai/)
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=== "Google Gemini"
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```shell
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pip install -U "langchain[google-genai]"
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```
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```python
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import os
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from langchain.chat_models import init_chat_model
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os.environ["GOOGLE_API_KEY"] = "..."
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llm = init_chat_model("google_genai:gemini-2.0-flash")
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```
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👉 Read the [Google GenAI integration docs](https://python.langchain.com/docs/integrations/chat/google_generative_ai/)
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=== "AWS Bedrock"
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```shell
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pip install -U "langchain[aws]"
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```
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```python
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from langchain.chat_models import init_chat_model
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# Follow the steps here to configure your credentials:
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# https://docs.aws.amazon.com/bedrock/latest/userguide/getting-started.html
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llm = init_chat_model(
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"anthropic.claude-3-5-sonnet-20240620-v1:0",
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model_provider="bedrock_converse",
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)
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```
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👉 Read the [AWS Bedrock integration docs](https://python.langchain.com/docs/integrations/chat/bedrock/)
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@@ -63,7 +63,7 @@ Next, add a "`chatbot`" node. **Nodes** represent units of work and are typicall
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Let's first select a chat model:
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{!snippets/chat_model_tabs.md!}
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{% include-markdown "../../../snippets/chat_model_tabs.md" %}
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<!---
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```python
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@@ -73,7 +73,7 @@ For the `StateGraph` you created in the [first tutorial](./1-build-basic-chatbot
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Let's first select our LLM:
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{!snippets/chat_model_tabs.md!}
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{% include-markdown "../../../snippets/chat_model_tabs.md" %}
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<!---
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```python
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@@ -286,7 +286,7 @@ For ease of use, adjust your code to replace the following with LangGraph prebui
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- `BasicToolNode` is replaced with the prebuilt [ToolNode](https://langchain-ai.github.io/langgraph/reference/prebuilt/#toolnode)
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- `route_tools` is replaced with the prebuilt [tools_condition](https://langchain-ai.github.io/langgraph/reference/prebuilt/#tools_condition)
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{!snippets/chat_model_tabs.md!}
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{% include-markdown "../../../snippets/chat_model_tabs.md" %}
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```python hl_lines="25 30"
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@@ -154,7 +154,7 @@ The snapshot above contains the current state values, corresponding config, and
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Check out the code snippet below to review the graph from this tutorial:
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{!snippets/chat_model_tabs.md!}
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{% include-markdown "../../../snippets/chat_model_tabs.md" %}
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<!---
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```python
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@@ -14,7 +14,7 @@ Starting with the existing code from the [Add memory to the chatbot](./3-add-mem
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Let's first select a chat model:
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{!snippets/chat_model_tabs.md!}
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{% include-markdown "../../../snippets/chat_model_tabs.md" %}
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<!---
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```python
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@@ -221,7 +221,7 @@ The input has been received and processed as a tool message. Review this call's
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Check out the code snippet below to review the graph from this tutorial:
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{!snippets/chat_model_tabs.md!}
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{% include-markdown "../../../snippets/chat_model_tabs.md" %}
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```python
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from typing import Annotated
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@@ -221,7 +221,7 @@ Manual state updates will [generate a trace](https://smith.langchain.com/public/
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Check out the code snippet below to review the graph from this tutorial:
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{!snippets/chat_model_tabs.md!}
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{% include-markdown "../../../snippets/chat_model_tabs.md" %}
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<!---
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```python
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@@ -14,7 +14,7 @@ You can create these types of experiences using LangGraph's built-in **time trav
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Rewind your graph by fetching a checkpoint using the graph's `get_state_history` method. You can then resume execution at this previous point in time.
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{!snippets/chat_model_tabs.md!}
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{% include-markdown "../../../snippets/chat_model_tabs.md" %}
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<!---
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```python
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@@ -540,11 +540,11 @@
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"================================\u001B[1m System Message \u001B[0m================================\n",
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"================================\u001b[1m System Message \u001b[0m================================\n",
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"\n",
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"Given a user query, create a plan to solve it with the utmost parallelizability. Each plan should comprise an action from the following \u001B[33;1m\u001B[1;3m{num_tools}\u001B[0m types:\n",
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"\u001B[33;1m\u001B[1;3m{tool_descriptions}\u001B[0m\n",
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"\u001B[33;1m\u001B[1;3m{num_tools}\u001B[0m. join(): Collects and combines results from prior actions.\n",
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"Given a user query, create a plan to solve it with the utmost parallelizability. Each plan should comprise an action from the following \u001b[33;1m\u001b[1;3m{num_tools}\u001b[0m types:\n",
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"\u001b[33;1m\u001b[1;3m{tool_descriptions}\u001b[0m\n",
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"\u001b[33;1m\u001b[1;3m{num_tools}\u001b[0m. join(): Collects and combines results from prior actions.\n",
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"\n",
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" - An LLM agent is called upon invoking join() to either finalize the user query or wait until the plans are executed.\n",
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" - join should always be the last action in the plan, and will be called in two scenarios:\n",
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@@ -561,11 +561,11 @@
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" - Only use the provided action types. If a query cannot be addressed using these, invoke the join action for the next steps.\n",
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" - Never introduce new actions other than the ones provided.\n",
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"\n",
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"=============================\u001B[1m Messages Placeholder \u001B[0m=============================\n",
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"=============================\u001b[1m Messages Placeholder \u001b[0m=============================\n",
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"\n",
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"\u001B[33;1m\u001B[1;3m{messages}\u001B[0m\n",
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"\u001b[33;1m\u001b[1;3m{messages}\u001b[0m\n",
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"\n",
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"================================\u001B[1m System Message \u001B[0m================================\n",
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"================================\u001b[1m System Message \u001b[0m================================\n",
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"\n",
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"Remember, ONLY respond with the task list in the correct format! E.g.:\n",
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"idx. tool(arg_name=args)\n",
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@@ -605,7 +605,7 @@
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" llm: BaseChatModel, tools: Sequence[BaseTool], base_prompt: ChatPromptTemplate\n",
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"):\n",
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" tool_descriptions = \"\\n\".join(\n",
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" f\"{i+1}. {tool.description}\\n\"\n",
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" f\"{i + 1}. {tool.description}\\n\"\n",
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" for i, tool in enumerate(\n",
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" tools\n",
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" ) # +1 to offset the 0 starting index, we want it count normally from 1.\n",
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@@ -378,7 +378,7 @@
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"\n",
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"async def execute_step(state: PlanExecute):\n",
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" plan = state[\"plan\"]\n",
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" plan_str = \"\\n\".join(f\"{i+1}. {step}\" for i, step in enumerate(plan))\n",
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" plan_str = \"\\n\".join(f\"{i + 1}. {step}\" for i, step in enumerate(plan))\n",
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" task = plan[0]\n",
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" task_formatted = f\"\"\"For the following plan:\n",
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"{plan_str}\\n\\nYou are tasked with executing step {1}, {task}.\"\"\"\n",
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@@ -302,7 +302,7 @@
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"def format_docs(docs: List[Doc]) -> str:\n",
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" xml_table = \"<conversations>\\n\"\n",
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" for doc in docs:\n",
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" xml_table += f'<conv_summ id={doc[\"id\"]}>{doc[\"summary\"]}</conv_summ>\\n'\n",
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" xml_table += f\"<conv_summ id={doc['id']}>{doc['summary']}</conv_summ>\\n\"\n",
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" xml_table += \"</conversations>\"\n",
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" return xml_table\n",
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"\n",
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@@ -311,9 +311,9 @@
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" xml = \"<cluster_table>\\n\"\n",
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" for label in clusters:\n",
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" xml += \" <cluster>\\n\"\n",
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" xml += f' <id>{label[\"id\"]}</id>\\n'\n",
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" xml += f' <name>{label[\"name\"]}</name>\\n'\n",
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" xml += f' <description>{label[\"description\"]}</description>\\n'\n",
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" xml += f\" <id>{label['id']}</id>\\n\"\n",
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" xml += f\" <name>{label['name']}</name>\\n\"\n",
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" xml += f\" <description>{label['description']}</description>\\n\"\n",
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" xml += \" </cluster>\\n\"\n",
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" xml += \"</cluster_table>\"\n",
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" return xml\n",
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@@ -600,13 +600,13 @@
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" turns.append(\n",
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" f\"\"\"\n",
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"<human idx={idx}>\n",
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"{run.inputs['question']}\n",
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"{run.inputs[\"question\"]}\n",
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"</human>\"\"\"\n",
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" )\n",
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" if run.outputs and run.outputs[\"output\"]:\n",
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" turns.append(\n",
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" f\"\"\"<ai idx={idx+1}>\n",
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"{run.outputs['output']}\n",
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" f\"\"\"<ai idx={idx + 1}>\n",
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"{run.outputs[\"output\"]}\n",
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"</ai>\"\"\"\n",
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" )\n",
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" return {\n",
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